Topic: Information bottleneck

Workshop bringing together researchers working on AI for learning dynamics from data, with a special focus on information-theoretic methods. Topics include predictive information, the information bottleneck, latent-variable models, entropy and mutual-information estimation, world models of biological systems, and connections to dynamical systems and control theory. The workshop is part of the six-week NITMB program “Image-Based Scientific Machine Learning for Theories of Biological Dynamics Across Scales” (February 1–March 12, 2027). Program: AI in Biological Dynamics Scientific Focus

One of Tali's cherished goals was to transform biology into physics. In his view, biologists were far too enamored by the details of the specific models they studied, losing sight of the big principles that may govern the behavior of these models. One such big principle that he suggested was the 'information bottleneck (IB) principle'. The iIB principle is an information-theoretical approach for extracting the relevant information that one random variable carries about another. Tali applied the IB principle to numerous problems in biology, gaining important insights in the process. Here I will